In this interview with Christian Lutz, we discuss the reasons why many IIOT projects do not succeed or do not perform up to the expectations, what can be done to optimize IIoT data and examples of best practices.

Manufacturers’ IIoT implementations have seen mixed results. What is holding back more widespread success for industrial organizations hoping to capitalize on the promise of the IIoT?

Christian Lutz: It’s absolutely the case that IIoT implementations, taken as a whole, have faced a rather tough track record. A Cisco survey found that almost three out of four IoT projects are failing. Another research report from Microsoft found that 30% of IoT projects fail at the proof-of-concept stage. We need to get beyond this storyline.

I see three key challenges that continue to be responsible for IIoT projects that stall or fail. First of all, deployments nosedive when not supported by teams with the requisite expertise utilizing data at IIoT scale. Recruiting that expertise is a tall task in itself, given the scarcity of top talent in the still-emerging field. Second, IIoT projects require organizational leadership and cultures that are supportive and willing to adopt new factory practices proven out by the data, and too often fail due to simple institutional inertia.

However, the third and most fatal challenge occurs when manufacturers pursue IIoT projects in the absence of a data-first strategy – that is to say without the correct data-layer technologies and infrastructure required for those implementations to have any realistic chance at success. Without strategies in place that are specifically designed to account for unrelenting and high-velocity IIoT data, ensure data availability, and process data within milliseconds to deliver real-time insights, many IIoT implementations are unfortunately doomed before they begin.

In your opinion, why has achieving a “data first” strategy been a struggle for some?

Christian Lutz: Even when manufacturers understand that data-first practices are essential to IIoT success, actually introducing those practices can prove impossible when relying on traditional databases and infrastructure. IIoT-enabled factories create unprecedented volumes of time-series data, and require technology designed to provide highly-available and highly-performing data orchestration.

In order to optimize practices and reap the IIoT’s many benefits, manufacturers also need to combine operational data found in ERP systems with IIoT data. Given these intensive requirements, traditional solutions simply cannot support data-first practices effectively. The way forward for organizations currently using these tools is pursuing a new digital transformation – introducing built-for-IIoT data-layer technologies designed to properly enable and accelerate IIoT implementations.

Can you discuss a specific manufacturer you have familiarity with, in terms of how it has approached an IIoT transformation and how that played out?

Christian Lutz: It is not all doom and gloom for IIoT projects, and here are a couple industrial organizations proving that:

Gantner Instruments is a global provider of edge sensor technology that recently developed an IIoT solution that enables real-time monitoring and analysis of its sensor data. The company utilized CrateDB to handle hot storage of real-time data, such as event-based data logging that can include volumes surpassing 100,000 samples per second for minutes at a time. It also used Apache Kafka as a smart (and open source) choice for data streaming. With this IIoT implementation, Gantner can now provide customers with easy access to its massive troves of sensor data, with 24/7 availability.

ALPLA is another example of a big manufacturer that has recently executed an IIoT transformation and reaped the benefits. The global packaging provider operates 178 plants across 45 countries and sought to introduce IIoT-derived optimization across that vast manufacturing infrastructure. ALPLA initially deployed Microsoft SQL Server to store its IIoT sensor data, but quickly discovered that this traditional solution wasn’t up to the task, with simple queries actually taking minutes to complete. Switching to the Crate IoT Data Platform and CrateDB, ALPLA immediately reduced completion time on these queries down to milliseconds, while enabling the company to use lower-cost servers. With its migration complete, ALPLA now has a centralized “mission control” for all its IIoT data, and now routinely leverages IIoT insights to boost efficiency and reduce costs.

Crate.io is particularly aligned with Microsoft Azure – what makes Microsoft an optimal partner for IIoT deployments?

Christian Lutz: For our purposes, Microsoft Azure offers the global availability to provide our IIoT cloud service to customers wherever they have plants, and delivers valuable innovations at a pace we certainly appreciate. For instance, when it comes to our customers like ALPLA that have factories across the globe, Azure enables us to easily connect to all regions as needed. Azure is also the largest Platform-as-a-Service that meets our requirements from an IIoT perspective, and Microsoft frequently introduces new Azure capabilities that benefit us in terms of both offering our customers lower costs and enabling us to more quickly debut new product features ourselves.

Thank you very much for this interview Mr. Lutz!

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